WeSME: uncovering mutual exclusivity of cancer drivers and beyond

Kim, Yoo-Ah; Madan, Sanna; Przytycka, Teresa M.

doi:10.1093/bioinformatics/btw242

Cited by 81 publications

(90 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a cautionary note, TTN and Mucins are very long genes. Yet, the mutual exclusivity pattern of the genes is prominent even after considering the length of the genes, and our previous studies found that the increased mutations in TTN in breast cancer are possibly linked to APOBEC activity [16]. A network based analysis also provided supporting evidence of TTN mutations as a disease marker [27].…”

Section: Resultsmentioning

confidence: 87%

“…This relation can help identify cancer drivers, cancer-driving pathways, and cancer subtypes [1–10]. Although less studied, co-occurrence of mutations can also provide critical information about possible synergistic effects between pairs of genes [11–13] or underlying mutagenic processes [14–16]. …”

Section: Introductionmentioning

confidence: 99%

“…If patients were exposed to the same mutagen, the process might have left its footprint in regions susceptible to the mutagenic process. For example, we observed in the previous work that the co-occurrence of TP53 and TTN mutations in breast cancer patients was not statistically significant based on a test corrected with the patients’ mutation frequencies, although they were found to co-occur using an uncorrected Fisher’s exact test [16]. This suggests that the co-occurrence of mutations in TP53 and TTN are due to the fact that both genes were affected by a common mutagenic process acting on them, and not due to a benefit to cancer progression from mutations in both genes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions

et al. 2017

Self Cite

View full text Add to dashboard Cite

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun, in which the relations between modules are enriched with mutual exclusivity, while genes within each module are functionally related; (ii) BeME-WithCo, which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun, which ensures co-occurrence between modules, while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to other novel findings such as the interaction between NCOR2 and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergistic effects, including a potential synergy between mutations in TP53 and the metastasis related DCC gene. Overall, BeWith not only helped us uncover relations between potential driver genes and pathways, but also provided additional insights on patterns of the mutational landscape, going beyond cancer driving mutations. Implementation is available at https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/software/bewith.html

show abstract

Section: Resultsmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…In particular, the APOBEC mutational signature acts as a footprint of the activity of the APOBEC family of cytidine deaminases. Playing a key factor in many human cancers (Haradhvala et al, 2016;Kanu et al, 2016;Roberts et al, 2013), APOBEC activity has been proposed to be the direct cause of some cancer driving mutations (Burns et al, 2013;Henderson et al, 2014;Kim et al, 2017). As another example, a recent analysis of mutational signatures in the genomes of tobacco smoking-associated cancers provided a novel insight into how smoking increases cancer risk (Alexandrov et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Detecting presence of mutational signatures in cancer with confidence

2017

View full text Add to dashboard Cite

Motivation: Cancers arise as the result of somatically acquired changes in the DNA of cancer cells. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of somatic mutations resulting from normal DNA damage and repair processes as well as carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. The decomposition of a cancer genome's mutation catalog into mutations consistent with such signatures can provide valuable information about cancer etiology. However, the results from different decomposition methods are not always consistent. Hence, one needs to be able to not only decompose a patient's mutational profile into signatures but also establish the accuracy of such decomposition. Results: We proposed two complementary ways of measuring confidence and stability of decomposition results and applied them to analyze mutational signatures in breast cancer genomes. We identified both very stable and highly unstable signatures, as well as signatures that previously have not been associated with breast cancer. We also provided additional support for the novel signatures. Our results emphasize the importance of assessing the confidence and stability of inferred signature contributions. Availability and implementation: All tools developed in this paper have been implemented in an R package, called SignatureEstimation, which is available from https://www.ncbi.nlm.nih.gov/ CBBresearch/Przytycka/index.cgi\#signatureestimation.

show abstract

“…Alexandrov et al 26 characterized dozens of ‘mutation signatures’ defining the nucleotide biases exhibited in subsets of cancers, some with known environmental triggers 27 , others attributable to specific sources of somatic hypermutability 18 , and some of unknown cause. Mechanisms of hypermutability may vary by tumour and over time in ways that are not observed in species evolution 21,28–31 . Treatment creates another complication, as chemotherapy or radiation therapy can themselves cause double-strand breaks in the DNA 32 or other forms of hypermutation 33,34 , inducing new mutation signatures 26,30 .…”

mentioning

confidence: 99%

The evolution of tumour phylogenetics: principles and practice

2017

View full text Add to dashboard Cite

Rapid advances in high-throughput sequencing and a growing realization of the importance of evolutionary theory to cancer genomics have led to a proliferation of phylogenetic studies of tumour progression. These studies have yielded not only new insights but also a plethora of experimental approaches, sometimes reaching conflicting or poorly supported conclusions. Here, we consider this body of work in light of the key computational principles underpinning phylogenetic inference, with the goal of providing practical guidance on the design and analysis of scientifically rigorous tumour phylogeny studies. We survey the range of methods and tools available to the researcher, their key applications, and the various unsolved problems, closing with a perspective on the prospects and broader implications of this field.

show abstract

WeSME: uncovering mutual exclusivity of cancer drivers and beyond

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 81 publications

References 37 publications

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions

Detecting presence of mutational signatures in cancer with confidence

The evolution of tumour phylogenetics: principles and practice

Contact Info

Product

Resources

About